Yiannis N. Kaznessis

Biochemical reactions with oscillatory behavior play an essential role in synthetic biology at the microscopic scale. Although a robust stability theory for deterministic chemical oscillators in the macroscopic limit exists, the dynamical stability of stochastic oscillators is an object of ongoing research. The Chemical Master Equation along with kinetic Monte Carlo simulations constitute the most accurate approach to modeling microscopic systems. However, because of the challenges of solving the fully probabilistic model, most studies in stability analysis have focused on the description of externally disturbed oscillators. Here we apply the Maximum Entropy Principle as closure criterion for moment equations of oscillatory networks and perform the stability analysis of the internally disturbed Brusselator network. Particularly, we discuss the effects of kinetic and size parameters on the dynamics of this stochastic oscillatory system with intrinsic noise. Our numerical experiments reveal that changes in kinetic parameters lead to phenomenological and dynamical Hopf bifurcations, while reduced system sizes in the oscillatory region can reverse the stochastic Hopf dynamical bifurcations at the ensemble level. This is a unique feature of the stochastic dynamics of oscillatory systems, with an unknown parallel in the macroscopic limit.

The time evolution of stochastic reaction networks can be modeled with the chemical master equation of the probability distribution. Alternatively, the numerical problem can be reformulated in terms of probability moment equations. Herein we present a new alternative method for numerically solving the time evolution of stochastic reaction networks. Based on the assumption that the entropy of the reaction network is maximum, Lagrange multipliers are introduced. The proposed method derives equations that model the time derivatives of these Lagrange multipliers. We present detailed steps to transform moment equations to Lagrange multiplier equations. In order to demonstrate the method, we present examples of non-linear stochastic reaction networks of varying degrees of complexity, including multistable and oscillatory systems. We find that the new approach is as accurate and significantly more efficient than Gillespie’s original exact algorithm for systems with small number of interacting species. This work is a step towards solving stochastic reaction networks accurately and efficiently.

Vancomycin‐resistant Enterococcus (VRE) poses a serious threat in hospitals where they densely colonize the intestinal tracts of patients. In vulnerable hosts, these pathogens may translocate to the bloodstream and become lethal. The ability to selectively reduce VRE in the intestinal tracts of patients could potentially prevent many of these translocation events and reduce the spread of the pathogen. Herein, we have engineered Escherichia. coli Nissle 1917 to produce and secrete three antimicrobial peptides, Enterocin A, Enterocin B, and Hiracin JM79, to specifically target and kill Enterococcus. These peptides exhibited potent activity against both Enterococcus faecium and Enterococcus faecalis, the two most prominent species responsible for VRE infections. We first discuss the optimization of the system used to express and secrete the peptides. We then show that by simultaneously expressing these peptides, both E. faecium and E. faecalis were drastically inhibited. We then demonstrate a suppression of the development of resistance when supernatant from the E. coli producer strains was used to treat E. faecium. Finally, we tested the efficacy of the probiotic in a VRE colonization model in mice. These studies showed that administration of the engineered probiotic significantly reduced the levels of both E. faecium and E. faecalis in the feces of male Balb/cJ mice.

Deterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. For example, in the stochastic Schlögl model, the bimodal stationary probability distribution collapses to a unimodal distribution when the system size increases, even for kinetic constant values that result in two distinct stable solutions in the deterministic Schlögl model. Using zero-information (ZI) closure scheme, an algorithm for solving chemical master equations, we compute stationary probability distributions for varying system sizes of the Schlögl model. With ZI-closure, system sizes can be studied that have been previously unattainable by stochastic simulation algorithms. We observe and quantify paradoxical discrepancies between stochastic and deterministic models and explain this behavior by postulating that the entropy of non-equilibrium steady states (NESS) is maximum.

Modern large‐scale agricultural practices that incorporate high density farming with subtherapeutic antibiotic dosing are considered a major contributor to the rise of antibiotic‐resistant bacterial infections of humans with species of Salmonella being a leading agriculture‐based bacterial infection. Microcin J25, a potent and highly stable antimicrobial peptide active against Enterobacteriaceae, is a candidate antimicrobial against multiple Salmonella species. Emerging evidence supports the hypothesis that the composition of the microbiota of the gastrointestinal tract prevents a variety of diseases by preventing infectious agents from proliferating. Reducing clearance of off‐target bacteria may decrease susceptibility to secondary infection. Of the Enterobacteriaceae susceptible to microcin J25, Escherichia coli are the most abundant within the human gut. To explore the modulation of specificity, a collection of 207 mutants encompassing 12 positions in both the ring and loop of microcin J25 was built and tested for activity against Salmonella and E. coli strains. As has been found previously, mutational tolerance of ring residues was lower than loop residues, with 22% and 51% of mutations, respectively, retaining activity toward at least one target within the target organism test panel. The multitarget screening elucidated increased mutational tolerance at position G2, G3, and G14 than previously identified in panels composed of single targets. Multiple mutations conferred differential response between the different targets. Examination of specificity differences between mutants found that 30% showed significant improvements to specificity toward any of the targets. Generation and testing of a combinatorial library designed from the point‐mutant study revealed that microcin J25^I13T reduces off‐target activity toward commensal human‐derived E. coli isolates by 81% relative to Salmonella enterica serovar Enteritidis. These in vitro specificity improvements are likely to improve in vivo treatment efficacy by reducing clearance of commensal bacteria in the gastrointestinal tract of hosts.

Stochastic formalisms are necessary to describe the behavior of many biological systems. However, there remains a lack of numerical methods available to calculate the stationary probability distributions of stochastic reaction networks. We have previously development a numerical approach to calculate stationary probability distributions of stochastic networks, named ZI closure scheme. In this work, we present a free applications based on ZI-closure scheme, called ZICS.

Protegrin-1 (PG-1) is a cationic arginine-rich antimicrobial peptide. It is widely accepted that PG-1 induces membrane disruption by forming pores that lead to cell death. However, the insertion mechanism for these highly cationic peptides into the hydrophobic membrane environment is still poorly understood at the molecular scale. It has previously been determined that the association of arginine guanidinium and lipid phosphate groups results in strong bidentate bonds that stabilize peptide–lipid complexes. It has also been suggested that arginine residues are able to drag phosphate groups as they insert inside the membrane to form a toroidal pore. However, whether bidentate bonds play a significant role in inducing a pore formation remains unclear. To investigate the role of bidentate complexes in PG-1 translocation, we conducted molecular dynamics simulations. Two computational electroporation methods were implemented to examine the translocation process. We found that PG-1 could insert into the membrane, provided the external electric potential is large enough to first induce a water column or a pore within the lipid bilayer membrane. We also found that the highly charged PG-1 is capable in itself of inducing molecular electroporation. Substitution of arginines with charge-equivalent lysines showed a markedly reduced tendency for insertion. This indicates that the guanidinium group likely facilitates PG-1 translocation. Potential of mean force calculations suggests that peptide insertion inside the hydrophobic environment of the membrane core is not favored. We found that formation of a water column or a pore might be a prerequisite for PG-1 translocation. We also found that PG-1 can stabilize the pore after insertion. We suggest that PG-1 could be a pore inducer and stabilizer. This work sheds some light on PG-1 translocation mechanisms at the molecular level. Methods presented in this study may be extended to other arginine-rich antimicrobial and cell-penetrating peptides.